Central Tendancy - VDAB Vacatures

*The author of this computation has been verified*

R Software Module: /rwasp_centraltendency.wasp (opens new window with default values)

Title produced by software: Central Tendency

Date of computation: Fri, 12 Nov 2010 11:35:16 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Nov/12/t1289561681lvdwfmfg3eeeg8h.htm/, Retrieved Fri, 12 Nov 2010 12:34:41 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Nov/12/t1289561681lvdwfmfg3eeeg8h.htm/}, year = {2010}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2010}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

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Dataseries X:

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43880 43110 44496 44164 40399 36763 37903 35532 35533 32110 33374 35462 33508 36080 34560 38737 38144 37594 36424 36843 37246 38661 40454 44928 48441 48140 45998 47369 49554 47510 44873 45344 42413 36912 43452 42142 44382 43636 44167 44423 42868 43908 42013 38846 35087 33026 34646 37135 37985 43121 43722 43630 42234 39351 39327 35704 30466 28155 29257 29998 32529 34787 33855 34556 31348 30805 28353 24514 21106 21346 23335 24379 26290 30084 29429 30632 27349 27264 27474 24482 21453 18788 19282 19713 21917 23812 23785 24696 24562 23580 24939 23899 21454 19761 19815 20780 23462 25005 24725 26198 27543 26471 26558 25317 22896 22248 23406 25073 27691 30599 31948 32946 34012 32936 32974 30951 29812 29010 31068 32447 34844 35676 35387 36488 35652 33488 32914 29781 27951

Output produced by software:

Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 2 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 33168.7596899225 698.510221057225 47.4850026385013 Geometric Mean 32200.4769383005 Harmonic Mean 31218.6303457759 Quadratic Mean 34097.2130102999 Winsorized Mean ( 1 / 43 ) 33163.9612403101 696.374010604736 47.6237779343751 Winsorized Mean ( 2 / 43 ) 33165.9767441860 694.555148090134 47.7513943066794 Winsorized Mean ( 3 / 43 ) 33152.4418604651 691.964874706715 47.9105848754484 Winsorized Mean ( 4 / 43 ) 33149.7441860465 691.006276709848 47.9731448227728 Winsorized Mean ( 5 / 43 ) 33134.007751938 677.303062631647 48.9205048375191 Winsorized Mean ( 6 / 43 ) 33118.7519379845 670.738867244069 49.3765212594028 Winsorized Mean ( 7 / 43 ) 33109.2015503876 665.756139261677 49.7317254739937 Winsorized Mean ( 8 / 43 ) 33112.4263565891 664.37087249726 49.8402740507347 Winsorized Mean ( 9 / 43 ) 33086.1937984496 660.768303551541 50.0723076767087 Winsorized Mean ( 10 / 43 ) 33116.4263565891 655.139999312014 50.5486253188111 Winsorized Mean ( 11 / 43 ) 33141.1550387597 650.938566327843 50.912876810664 Winsorized Mean ( 12 / 43 ) 33181.4341085271 640.526738337148 51.8033551490269 Winsorized Mean ( 13 / 43 ) 33225.3720930233 635.019218189254 52.3218371055995 Winsorized Mean ( 14 / 43 ) 33205.2945736434 630.370345573057 52.6758512782786 Winsorized Mean ( 15 / 43 ) 33208.5503875969 629.149235502274 52.7832643094372 Winsorized Mean ( 16 / 43 ) 33203.5891472868 624.795785591871 53.1431067766134 Winsorized Mean ( 17 / 43 ) 33219.2713178295 620.075653239427 53.5729328256703 Winsorized Mean ( 18 / 43 ) 33222.2015503876 619.518326447837 53.6258575930681 Winsorized Mean ( 19 / 43 ) 33208.7984496124 614.572341615659 54.0356215222919 Winsorized Mean ( 20 / 43 ) 33231.8992248062 599.222831303876 55.4583328417167 Winsorized Mean ( 21 / 43 ) 33246.8759689922 597.062659732843 55.6840650257188 Winsorized Mean ( 22 / 43 ) 33211.0620155039 591.13908892119 56.1814683514045 Winsorized Mean ( 23 / 43 ) 33138.496124031 579.906981201657 57.1445028224405 Winsorized Mean ( 24 / 43 ) 33130.1240310078 572.88516090339 57.8303057784992 Winsorized Mean ( 25 / 43 ) 33117.9147286822 570.029370761619 58.0986111021493 Winsorized Mean ( 26 / 43 ) 33135.0465116279 561.878277303355 58.9719301316546 Winsorized Mean ( 27 / 43 ) 32822.5581395349 521.433242091186 62.9468079325005 Winsorized Mean ( 28 / 43 ) 32825.3798449612 518.342693043337 63.3275635704134 Winsorized Mean ( 29 / 43 ) 32644.6356589147 485.729405104116 67.2074519596304 Winsorized Mean ( 30 / 43 ) 32843.9379844961 461.509563417663 71.1663215411478 Winsorized Mean ( 31 / 43 ) 32750.4573643411 446.50679401735 73.3481725321037 Winsorized Mean ( 32 / 43 ) 32768.3178294574 438.571266100167 74.7160618178148 Winsorized Mean ( 33 / 43 ) 32771.1317829457 434.014204264529 75.5070489881292 Winsorized Mean ( 34 / 43 ) 32820.9457364341 399.150333398206 82.2270282402365 Winsorized Mean ( 35 / 43 ) 32800.8682170543 392.168709613457 83.6396872391593 Winsorized Mean ( 36 / 43 ) 32812.8682170543 386.02938250156 85.0009603010509 Winsorized Mean ( 37 / 43 ) 32744.0310077519 374.841938416425 87.3542356175083 Winsorized Mean ( 38 / 43 ) 32685.1162790698 359.857074985528 90.8280496649517 Winsorized Mean ( 39 / 43 ) 32730.1627906977 348.058594871596 94.036358455025 Winsorized Mean ( 40 / 43 ) 32724.2713178295 334.489315884505 97.8335323844208 Winsorized Mean ( 41 / 43 ) 32765.2713178295 325.655146678403 100.613399333702 Winsorized Mean ( 42 / 43 ) 32953.1317829457 300.804127277037 109.550131779263 Winsorized Mean ( 43 / 43 ) 32943.7984496124 283.387972397719 116.249811771749 Trimmed Mean ( 1 / 43 ) 33152.9763779528 688.304811289239 48.1661261612498 Trimmed Mean ( 2 / 43 ) 33141.64 679.421956533166 48.7791712960077 Trimmed Mean ( 3 / 43 ) 33128.8780487805 670.672399231706 49.3965132406396 Trimmed Mean ( 4 / 43 ) 33120.5041322314 662.04642970561 50.0274643078416 Trimmed Mean ( 5 / 43 ) 33112.5798319328 652.808944443597 50.7232324461414 Trimmed Mean ( 6 / 43 ) 33107.8547008547 646.124881315812 51.2406435013486 Trimmed Mean ( 7 / 43 ) 33105.8173913044 640.141724580451 51.716387356256 Trimmed Mean ( 8 / 43 ) 33105.2654867257 634.437419022869 52.18050590035 Trimmed Mean ( 9 / 43 ) 33104.2252252252 628.292942740545 52.689156559404 Trimmed Mean ( 10 / 43 ) 33106.5963302752 622.001560067999 53.2259056177543 Trimmed Mean ( 11 / 43 ) 33105.4112149533 615.811264210694 53.7590218610009 Trimmed Mean ( 12 / 43 ) 33101.4190476190 609.457407422137 54.3129325273612 Trimmed Mean ( 13 / 43 ) 33093.0679611650 603.743456993283 54.8131289504529 Trimmed Mean ( 14 / 43 ) 33080.0693069307 597.99162586564 55.3186163084554 Trimmed Mean ( 15 / 43 ) 33068.4141414141 592.056788299093 55.8534498631722 Trimmed Mean ( 16 / 43 ) 33055.9896907216 585.446486166499 56.462871452474 Trimmed Mean ( 17 / 43 ) 33043.4631578947 578.457275382069 57.123429100394 Trimmed Mean ( 18 / 43 ) 33029.1182795699 571.055832945158 57.8386847205918 Trimmed Mean ( 19 / 43 ) 33013.9120879121 562.655200056839 58.6752101190517 Trimmed Mean ( 20 / 43 ) 32999.0449438202 553.651448736455 59.6025622603007 Trimmed Mean ( 21 / 43 ) 32981.7816091954 545.198147123761 60.495036131714 Trimmed Mean ( 22 / 43 ) 32962.6235294118 535.713223372814 61.5303526052266 Trimmed Mean ( 23 / 43 ) 32945.0722891566 525.513857303295 62.6911580566423 Trimmed Mean ( 24 / 43 ) 32931.6790123457 515.147953045435 63.9266424677828 Trimmed Mean ( 25 / 43 ) 32918.1772151899 504.004034813133 65.3133208098122 Trimmed Mean ( 26 / 43 ) 32904.7922077922 491.390469051927 66.9626178775459 Trimmed Mean ( 27 / 43 ) 32889.56 477.723600301141 68.8464207739946 Trimmed Mean ( 28 / 43 ) 32893.9452054795 467.194674341471 70.407363379826 Trimmed Mean ( 29 / 43 ) 32898.3943661972 455.181928595887 72.2752646786305 Trimmed Mean ( 30 / 43 ) 32914.7536231884 445.352312427935 73.9072251443951 Trimmed Mean ( 31 / 43 ) 32919.2985074627 436.975965159479 75.3343458957713 Trimmed Mean ( 32 / 43 ) 32930.1076923077 428.910472953473 76.7761800395111 Trimmed Mean ( 33 / 43 ) 32940.4603174603 420.251620324971 78.3827086543728 Trimmed Mean ( 34 / 43 ) 32951.3114754098 410.376565403327 80.2953049792805 Trimmed Mean ( 35 / 43 ) 32959.6949152542 403.4708396427 81.6904015775767 Trimmed Mean ( 36 / 43 ) 32969.9649122807 395.907713865005 83.2768944823406 Trimmed Mean ( 37 / 43 ) 32980.2 387.418794271628 85.1280332488899 Trimmed Mean ( 38 / 43 ) 32995.7358490566 378.539934441933 87.1657990264646 Trimmed Mean ( 39 / 43 ) 33016.4117647059 369.783885137029 89.28569656969 Trimmed Mean ( 40 / 43 ) 33035.7346938776 360.724805098276 91.5815442325274 Trimmed Mean ( 41 / 43 ) 33057.1063829787 351.539088895269 94.0353645646135 Trimmed Mean ( 42 / 43 ) 33077.5111111111 341.342444276816 96.9041842457966 Trimmed Mean ( 43 / 43 ) 33086.3953488372 333.251512338167 99.2835564846973 Median 33026 Midrange 34171 Midmean - Weighted Average at Xnp 32839.375 Midmean - Weighted Average at X(n+1)p 32930.1076923077 Midmean - Empirical Distribution Function 32930.1076923077 Midmean - Empirical Distribution Function - Averaging 32930.1076923077 Midmean - Empirical Distribution Function - Interpolation 32930.1076923077 Midmean - Closest Observation 32829.5 Midmean - True Basic - Statistics Graphics Toolkit 32930.1076923077 Midmean - MS Excel (old versions) 32930.1076923077 Number of observations 129

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/12/t1289561681lvdwfmfg3eeeg8h/1c51d1289561712.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/12/t1289561681lvdwfmfg3eeeg8h/1c51d1289561712.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/12/t1289561681lvdwfmfg3eeeg8h/2c51d1289561712.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/12/t1289561681lvdwfmfg3eeeg8h/2c51d1289561712.ps (open in new window)

Parameters (Session):

Parameters (R input):

R code (references can be found in the software module):

geomean <- function(x) { return(exp(mean(log(x)))) } harmean <- function(x) { return(1/mean(1/x)) } quamean <- function(x) { return(sqrt(mean(x*x))) } winmean <- function(x) { x <-sort(x[!is.na(x)]) n<-length(x) denom <- 3 nodenom <- n/denom if (nodenom>40) denom <- n/40 sqrtn = sqrt(n) roundnodenom = floor(nodenom) win <- array(NA,dim=c(roundnodenom,2)) for (j in 1:roundnodenom) { win[j,1] <- (j*x[j+1]+sum(x[(j+1):(n-j)])+j*x[n-j])/n win[j,2] <- sd(c(rep(x[j+1],j),x[(j+1):(n-j)],rep(x[n-j],j)))/sqrtn } return(win) } trimean <- function(x) { x <-sort(x[!is.na(x)]) n<-length(x) denom <- 3 nodenom <- n/denom if (nodenom>40) denom <- n/40 sqrtn = sqrt(n) roundnodenom = floor(nodenom) tri <- array(NA,dim=c(roundnodenom,2)) for (j in 1:roundnodenom) { tri[j,1] <- mean(x,trim=j/n) tri[j,2] <- sd(x[(j+1):(n-j)]) / sqrt(n-j*2) } return(tri) } midrange <- function(x) { return((max(x)+min(x))/2) } q1 <- function(data,n,p,i,f) { np <- n*p; i <<- floor(np) f <<- np - i qvalue <- (1-f)*data[i] + f*data[i+1] } q2 <- function(data,n,p,i,f) { np <- (n+1)*p i <<- floor(np) f <<- np - i qvalue <- (1-f)*data[i] + f*data[i+1] } q3 <- function(data,n,p,i,f) { np <- n*p i <<- floor(np) f <<- np - i if (f==0) { qvalue <- data[i] } else { qvalue <- data[i+1] } } q4 <- function(data,n,p,i,f) { np <- n*p i <<- floor(np) f <<- np - i if (f==0) { qvalue <- (data[i]+data[i+1])/2 } else { qvalue <- data[i+1] } } q5 <- function(data,n,p,i,f) { np <- (n-1)*p i <<- floor(np) f <<- np - i if (f==0) { qvalue <- data[i+1] } else { qvalue <- data[i+1] + f*(data[i+2]-data[i+1]) } } q6 <- function(data,n,p,i,f) { np <- n*p+0.5 i <<- floor(np) f <<- np - i qvalue <- data[i] } q7 <- function(data,n,p,i,f) { np <- (n+1)*p i <<- floor(np) f <<- np - i if (f==0) { qvalue <- data[i] } else { qvalue <- f*data[i] + (1-f)*data[i+1] } } q8 <- function(data,n,p,i,f) { np <- (n+1)*p i <<- floor(np) f <<- np - i if (f==0) { qvalue <- data[i] } else { if (f == 0.5) { qvalue <- (data[i]+data[i+1])/2 } else { if (f < 0.5) { qvalue <- data[i] } else { qvalue <- data[i+1] } } } } midmean <- function(x,def) { x <-sort(x[!is.na(x)]) n<-length(x) if (def==1) { qvalue1 <- q1(x,n,0.25,i,f) qvalue3 <- q1(x,n,0.75,i,f) } if (def==2) { qvalue1 <- q2(x,n,0.25,i,f) qvalue3 <- q2(x,n,0.75,i,f) } if (def==3) { qvalue1 <- q3(x,n,0.25,i,f) qvalue3 <- q3(x,n,0.75,i,f) } if (def==4) { qvalue1 <- q4(x,n,0.25,i,f) qvalue3 <- q4(x,n,0.75,i,f) } if (def==5) { qvalue1 <- q5(x,n,0.25,i,f) qvalue3 <- q5(x,n,0.75,i,f) } if (def==6) { qvalue1 <- q6(x,n,0.25,i,f) qvalue3 <- q6(x,n,0.75,i,f) } if (def==7) { qvalue1 <- q7(x,n,0.25,i,f) qvalue3 <- q7(x,n,0.75,i,f) } if (def==8) { qvalue1 <- q8(x,n,0.25,i,f) qvalue3 <- q8(x,n,0.75,i,f) } midm <- 0 myn <- 0 roundno4 <- round(n/4) round3no4 <- round(3*n/4) for (i in 1:n) { if ((x[i]>=qvalue1) & (x[i]<=qvalue3)){ midm = midm + x[i] myn = myn + 1 } } midm = midm / myn return(midm) } (arm <- mean(x)) sqrtn <- sqrt(length(x)) (armse <- sd(x) / sqrtn) (armose <- arm / armse) (geo <- geomean(x)) (har <- harmean(x)) (qua <- quamean(x)) (win <- winmean(x)) (tri <- trimean(x)) (midr <- midrange(x)) midm <- array(NA,dim=8) for (j in 1:8) midm[j] <- midmean(x,j) midm bitmap(file='test1.png') lb <- win[,1] - 2*win[,2] ub <- win[,1] + 2*win[,2] if ((ylimmin == '') | (ylimmax == '')) plot(win[,1],type='b',main=main, xlab='j', pch=19, ylab='Winsorized Mean(j/n)', ylim=c(min(lb),max(ub))) else plot(win[,1],type='l',main=main, xlab='j', pch=19, ylab='Winsorized Mean(j/n)', ylim=c(ylimmin,ylimmax)) lines(ub,lty=3) lines(lb,lty=3) grid() dev.off() bitmap(file='test2.png') lb <- tri[,1] - 2*tri[,2] ub <- tri[,1] + 2*tri[,2] if ((ylimmin == '') | (ylimmax == '')) plot(tri[,1],type='b',main=main, xlab='j', pch=19, ylab='Trimmed Mean(j/n)', ylim=c(min(lb),max(ub))) else plot(tri[,1],type='l',main=main, xlab='j', pch=19, ylab='Trimmed Mean(j/n)', ylim=c(ylimmin,ylimmax)) lines(ub,lty=3) lines(lb,lty=3) grid() dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Central Tendency - Ungrouped Data',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Measure',header=TRUE) a<-table.element(a,'Value',header=TRUE) a<-table.element(a,'S.E.',header=TRUE) a<-table.element(a,'Value/S.E.',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/arithmetic_mean.htm', 'Arithmetic Mean', 'click to view the definition of the Arithmetic Mean'),header=TRUE) a<-table.element(a,arm) a<-table.element(a,hyperlink('http://www.xycoon.com/arithmetic_mean_standard_error.htm', armse, 'click to view the definition of the Standard Error of the Arithmetic Mean')) a<-table.element(a,armose) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/geometric_mean.htm', 'Geometric Mean', 'click to view the definition of the Geometric Mean'),header=TRUE) a<-table.element(a,geo) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/harmonic_mean.htm', 'Harmonic Mean', 'click to view the definition of the Harmonic Mean'),header=TRUE) a<-table.element(a,har) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/quadratic_mean.htm', 'Quadratic Mean', 'click to view the definition of the Quadratic Mean'),header=TRUE) a<-table.element(a,qua) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) for (j in 1:length(win[,1])) { a<-table.row.start(a) mylabel <- paste('Winsorized Mean (',j) mylabel <- paste(mylabel,'/') mylabel <- paste(mylabel,length(win[,1])) mylabel <- paste(mylabel,')') a<-table.element(a,hyperlink('http://www.xycoon.com/winsorized_mean.htm', mylabel, 'click to view the definition of the Winsorized Mean'),header=TRUE) a<-table.element(a,win[j,1]) a<-table.element(a,win[j,2]) a<-table.element(a,win[j,1]/win[j,2]) a<-table.row.end(a) } for (j in 1:length(tri[,1])) { a<-table.row.start(a) mylabel <- paste('Trimmed Mean (',j) mylabel <- paste(mylabel,'/') mylabel <- paste(mylabel,length(tri[,1])) mylabel <- paste(mylabel,')') a<-table.element(a,hyperlink('http://www.xycoon.com/arithmetic_mean.htm', mylabel, 'click to view the definition of the Trimmed Mean'),header=TRUE) a<-table.element(a,tri[j,1]) a<-table.element(a,tri[j,2]) a<-table.element(a,tri[j,1]/tri[j,2]) a<-table.row.end(a) } a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/median_1.htm', 'Median', 'click to view the definition of the Median'),header=TRUE) a<-table.element(a,median(x)) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/midrange.htm', 'Midrange', 'click to view the definition of the Midrange'),header=TRUE) a<-table.element(a,midr) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_1.htm','Weighted Average at Xnp',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[1]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_2.htm','Weighted Average at X(n+1)p',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[2]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_3.htm','Empirical Distribution Function',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[3]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_4.htm','Empirical Distribution Function - Averaging',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[4]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_5.htm','Empirical Distribution Function - Interpolation',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[5]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_6.htm','Closest Observation',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[6]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_7.htm','True Basic - Statistics Graphics Toolkit',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[7]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_8.htm','MS Excel (old versions)',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[8]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Number of observations',header=TRUE) a<-table.element(a,length(x)) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable.tab')

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